Whole-brain computational anatomy in graves

Tasnim Ahmed; Bahareh Arjomand; Rolf Heckemann; Mats Holmberg; Karin Tammelin; Nystrom Helena Filipsson

doi:10.1530/endoabs.101.OP-10-05

OP-10-05

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Endocrine Abstracts (2024) 101 OP10-05 | DOI: 10.1530/endoabs.101.OP-10-05

ETA2024 Oral Presentations Oral Session 10: Thyroid dysfunction-2 (5 abstracts)

Whole-brain computational anatomy in graves’ disease: morphometric imprint of pathology and recovery

Tasnim Ahmed ¹ , Bahareh Arjomand ¹ , Rolf Heckemann ² , Mats Holmberg ³ , Karin Tammelin ⁴ & Helena Filipsson Nyström ⁵

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¹Chalmers University of Technology, Göteborg, Sweden; ²Institute of Clinical Sciences, University of Gothenburg, Department of Medical Radiation Sciences, Göteborg, Sweden; ³Karolinska Institutet, Dept of Medicine, Huddinge, Huddinge, Sweden; ⁴Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Göteborg, Sweden, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Göteborg, Sweden, Department of Endocrinology, Sahlgrenska University Hospital, Göteborg, Sweden, Gothenburg, Sweden; ⁵Department of Endocrinology, Inst of Medicine, Sahlgrenska Academ, University of Gothenburg, Göteborg, Sweden

Objectives: In CogThy, a longitudinal study on Graves’ disease, we previously showed that medial temporal lobe volumes (hippocampus and amygdala) are smaller in hyperthyroid patients than in matched thyroid-healthy participants. Longitudinally, we saw a volume increase in the patients in the sense of a recovery of the brain under treatment. In the present work, we conducted a corresponding analysis on the whole brain.

Methods: Automatic segmentations of T1-weighted images (baseline and 15 months after diagnosis) were carried out with MAPER software and the Hammers Atlas Database, augmented to 120 subcortical and cortical regions. We considered regional context class components (background, cerebrospinal fluid, grey matter (GM), white matter (WM), GM and WM summation (GMWM), total volume). Using a split-sample approach, we separated the exploration (hypothesis generation) from the validation. During the exploration, we identified clusters of regions using prior knowledge (“natural clusters”) and a data-driven approach (affine propagation (AP)). For entities showing notable differences at baseline, we formulated hypotheses and tested them on the validation set, claiming significance for differences with P < 0.05 after accounting for multiple comparisons (Benjamini-Hochberg).

Results: At baseline at diagnosis in severe hyperthyroidism (FT4>50 pmol/l), several entities were significantly different between patients and controls: five individual regions, seven natural clusters, and one AP cluster (cf. Table). None of these entities differed between patients and controls at follow-up, suggesting a recovery effect.Table 1 Adj: Benjamini-Hochberg adjusted. indiv: Individual region. NC: natural cluster. AP: data-driven cluster. Diff %: patient/control volume difference (positive if entity is larger in patients).

Table 1 Adj: Benjamini-Hochberg adjusted. indiv: Individual region. NC: natural cluster. AP: data-driven cluster. Diff %: patient/control volume difference (positive if entity is larger in patients).
Entity	Adj P-value	Type	Diff %
Gyrus parahippocampalis et ambiens left GMWM	0.006	indiv	-9.9
Middle frontal gyrus left total	0.016	indiv	5.5.
Inferior frontal gyrus right GM	0.069	indiv	12.4.
Medial orbital gyrus left total	0.021	indiv	10.3.
Subgenual frontal cortex right WM	0.030	indiv	10.1.
Frontal lobe bilateral	0.043	NC	3.7
Frontal lobe left	0.021	NC	6.5
Top 3 frontal lobe WM left	0.030	NC	5.6
Temporal lobe left GMWM	0.006	NC	-11.3
Medial temporal lobe bilateral GMWM	0.006	NC	-5.7
All regions larger in patients on exploration	0.006	NC	1.9
All regions smaller in patients on exploration	0.006	NC	-6.3
Medial orbital gyrus right (*)	0.021	AP	9.0

Conclusion: Graves’ disease produces a morphometric imprint in the patient’s brain beyond the medial temporal lobe, namely in the frontal lobe. This suggests that imaging biomarkers derived via quantitative image analysis should be further explored fo